CS 598: Graphical Models


  • Where: Siebel 1109

  • When: Tues & Thurs 3:30-4:45


Probabilistic Graphical Models are efficient representations of joint distributions using graphs, with a range of applications to machine learning, computer vision, natural language processing and computational biology, among other fields. The course will cover the fundamentals of probabilistic graphical models, including techniques for inferring properties of the distribution given the graph structure and parameters, and for learning the graphical model from data. The course will also cover selected special topics such as approximate inference, and learning high dimensional models subject to sparsity assumptions.

Prerequisites: Background in basic probability theory, statistics & linear algebra. Familiarity with basics of data analysis. You should be comfortable solving all of these problems (pdf).


  • First day of class: Aug 23, 2016 @ 3:30 - 5:00 PM.

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